class_docstr: NextItNet Model
  
  :Citation:
      Yuan, Fajie, et al. "A Simple Convolutional Generative Network
      for Next Item Recommendation", in Web Search and Data Mining, 2019.
  
  .. note::
  
      It requires strong sequence with dataset.
docstr: Call a layer normalization
  
  Returns:
      object: Normalized data
docstr_tok: ["Call","a","layer","normalization","Returns",":","object",":","Normalized","data"]
code: def _layer_norm(self, x, name, epsilon=1e-8, trainable=True):
      """Call a layer normalization
  
      Returns:
          object: Normalized data
      """
      with tf.compat.v1.variable_scope(name):
          shape = x.get_shape()
          beta = tf.compat.v1.get_variable(
              "beta",
              [int(shape[-1])],
              initializer=tf.compat.v1.constant_initializer(0),
              trainable=trainable,
          )
          gamma = tf.compat.v1.get_variable(
              "gamma",
              [int(shape[-1])],
              initializer=tf.compat.v1.constant_initializer(1),
              trainable=trainable,
          )
  
          mean, variance = tf.nn.moments(
              x=x, axes=[len(shape) - 1], keepdims=True
          )
  
          x = (x - mean) / tf.sqrt(variance + epsilon)
  
          return gamma * x + beta
  
file_path: recommenders/recommenders/models/deeprec/models/sequential/nextitnet.py
imports: import tensorflow
  from recommenders.models.deeprec.models.sequential.sequential_base_model import SequentialBaseModel
label_desc: Use image node_id 6 for calling the NextItNetModel obj's underlying member method code with example usage: obj._layer_norm(x, name, epsilon, trainable) and returns: unknown